In the era featuring advanced technology and information explosion, how to accurately extract the required information from massive data has become the study target. As one of the important ways to solve this problem, question-answering systems mainly retrieve and analyze existing data and information and finally return the answer to the question or other related information. In recent years, the revolutionary development of deep learning has brought considerable progress to question-answering systems. Sequence-to-sequence models, end-to-end models, and the recently popular pre-training have left unlimited development space for the question-answering systems, but these systems still face many challenges. This study first briefly introduces the development of the question-answering systems, then classifies these systems from three different perspectives, and expounds on the relevant data sets, evaluation indicators, and mainstream technologies of various question-answering systems. Finally, the study discusses the problems faced by question-answering systems and their future development trends.